1. Field of the Invention
The present invention relates generally to a communication system. More particularly, the present invention relates to an apparatus and method for detecting a signal in a communication system using multiple antennas.
2. Description of the Related Art
In the field of communication system technologies, active research and development is being conducted with the goal of providing high-quality, high-speed and high-capacity data transmissions for multimedia services. Unlike a wired channel environment, a wireless channel environment, existing in such a communication system, may suffer from signal distortion due to several factors such as multipath interference, shadowing, propagation loss, time-varying noise, interference and the like. A received signal that suffers from such distortion during its transmission causes a reduction in the entire performance of the mobile communication system. As a result, the fading phenomenon, which is a distortion of the amplitude and phase of the received signal, can be a main cause of interruption of the high-speed data communication in the wireless channel environment. Accordingly, many attempts are being made to solve the fading phenomenon and the Multiple-Input Multiple-Output (MIMO) technology has been proposed as a solution.
The Vertical-Bell Labs Layered Space-Time (V-BLAST) communication system is one such MIMO-based communication system. In the V-BLAST communication system, a transmitter (or transmitting entity) uses a plurality of transmit antennas and transmits different independent data separately via each of the transmit antennas.
FIG. 1 schematically illustrates a configuration of a conventional V-BLAST communication system.
Referring to FIG. 1, an expected transmission signal is modulated by a modulator 102 and then transmitted via transmit antennas 104 and 106. The signal transmitted over a wireless channel is input to a detector 114 via a plurality of receive antennas 110 and 112. The detector 114 detects the original transmission signal using any one of various detection techniques such as a Maximum Likelihood Detection (MLD) technique. A concern with the MLD technique is that it exponentially increases in its complexity according to the number of antennas and a modulation order of the transmitter.
A QR Decomposition based M (QRD-M) algorithm has been proposed as a scheme for solving such a problem.
FIG. 2 illustrates a tree searching technique of a conventional QRD-M algorithm.
Referring to FIG. 2, there is shown a 3×3 V-BLAST system using Quadrature Phase Shift Keying (QPSK) as a modulation scheme, by way of example. A received signal is extended to 4 candidate symbols according to the modulation order (1st Stage). That is, M=4.
Among the extended 4 candidate symbols, 4 candidate symbols are selected in order of the lower accumulated metric and each of the selected candidate symbols is extended again to 4 branches and candidates (2nd Stage).
Among all the extended branches, 4 branches are selected in order of the lower metric, and each of the candidate symbols corresponding to the selected branches is extended again to 4 branches and candidates. In this manner, the candidate having the lowest accumulated metric among the last candidates is determined as a received symbol (3rd Stage).
Various QRD-M algorithms will be described hereinbelow.
First, a description will be made of a QRD-M algorithm proposed in a paper by Kyeong Jin Kim and Ronald A. Iltis, titled “Joint detection and channel estimation algorithms for QS-CDMA signals over time-varying Channels,” IEEE Transactions on communications, Vol. 50, NO. 5, May 2002.
A receiver (or receiving entity) generates a tree structure using a characteristic of an R matrix generated after performing QR decomposition on a channel. The number of stages of the tree is equal to the number of transmit antennas, and the number of branches that can be extended from the branches of each stage to the next stage is determined depending on the modulation order in use. All branches of the tree are searched using a Maximum Likelihood (ML) technique. However, for M, only M branches are selected in each stage, and the branches selected in the corresponding stage are extended to as many branches as the modulation order in the next stage. When a value of the M is equal to the modulation order, performance of the QRD-M algorithm approaches the ML performance.
However, the QRD-M algorithm also has problems. If the M value is less than the modulation order, the QRD-M algorithm suffers from performance degradation. Therefore, when the QRD-M algorithm uses a plurality of transmit antennas and employs a high modulation order, its complexity is much lower than the complexity O(MNt) for the case where it uses an ML receiver. However, the QRD-M algorithm still requires a large amount of calculation.
Second, a description will be made of a QRD-M algorithm (hereinafter referred to as a ‘Nokia QRD-M algorithm’) proposed in a paper by Kyeong Jin Kim, Jiang Yue, Ronald A. Iltis and Jerry D. Gibson, titled “A QRD-M/Kalman Filter-Based Detection and Channel Estimation Algorithm for MIMO-OFDM Systems,” IEEE Transactions on wireless communications, Vol. 4, NO. 2, March 2005.
The number ‘M’ of branches selected in each stage of the Nokia QRD-M algorithm has an adaptive value rather than a constant value. That is, a lower M value is determined for the signal having a higher channel gain and a higher M value is determined for the signal having a lower channel gain. For determination of the M value, a receiver finds a Probability Density Function (PDF) for a square of R11 corresponding to the first stage of the tree structure through QR decomposition of a channel matrix. Of course, the receiver should find accumulated metrics for all possible candidate symbols in the first stage.
The maximum value {circumflex over (M)} of the candidate symbols selected in each stage is predetermined and the receiver divides the found PDF into {circumflex over (M)} sections using a Lloyd-Max algorithm. Thereafter, the receiver finds power of a signal detected in each stage and selects as many paths as the number of candidate symbols having the lower accumulated metric from among the candidate symbols corresponding to the divided PDF sections. The receiver selects {circumflex over (M)} candidate symbols in the section having the lowest signal power and selects the candidate symbols corresponding to the value decreased by one in the next section. The sections can be divided as Equation (1).ΔRε[0th1):M={circumflex over (M)}, ΔRε[th1,th2):M={circumflex over (M)}−1, . . .   (1)ΔRε[th{circumflex over (M)}−1,th{circumflex over (M)}):M=1
If the {circumflex over (M)} value is equal to a value of the modulation order, the Nokia QRD-M algorithm approaches the ML performance in terms of the performance while requiring 75% complexity for the case of M=16 of the QRD-M algorithm.
However, the Nokia QRD-M algorithm also has the following problem.
To find the PDF for a square of R11 used for determining candidate symbols that will survive in each stage, the Nokia QRD-M algorithm needs training symbols. Because the Nokia QRD-M algorithm should use the training symbols several times to find the PDF for the square of R11, its complexity may further increase. The PDF for the square of R11 found for signal detection in the first stage after QR decomposition on the channel matrix is used intact even in the remaining stages of the tree structure, making it difficult to detect the optimal number of candidates.
Finally, a description will be made of a QRD-M algorithm (hereinafter referred to as an ‘NTT DoCoMo QRD-M algorithm’) proposed in a paper by Hiroyuki Kawai, Kenichi Higuchi, Noriyuki Maeda and Mamoru Sawahashi, titled “Independent Adaptive Control of Surviving Symbol Replica Candidates at Each Stage Based on Minimum Branch Metric in QRM-MLD for OFCDM MIMO Multiplexing,” NTT DoCoMo, Inc.
The number of candidates for each stage of the NTT DoCoMo QRD-M algorithm can be set in a different manner. A receiver, after generating a tree structure through QR decomposition on a channel matrix, finds accumulated metrics for all possible candidate symbols in the first stage. A threshold is determined by selecting the lowest accumulated metric from among the accumulated metrics and multiplying estimated noise power by a predetermined constant X. A threshold in each stage can be determined using Equation (2).Δn=En,min+Xσ2  (2)
In Equation (2), En,min denotes the minimum accumulated metric in an nth stage, X denotes a predetermined value and σ2 denotes noise power. The maximum number {circumflex over (M)} of candidate symbols selectable in each stage is predetermined. In the first stage, the receiver selects candidate symbols having accumulated metrics lower than the threshold. Thereafter, from the second stage on, the threshold is determined using the lowest accumulated metric and the estimated noise power. In the last stage, the candidate symbol having the lowest accumulated metric among the surviving branches is estimated as a transmission signal.
It can be noted that the NTT DoCoMo QRD-M algorithm needs the lowest accumulated metric and the estimated noise power to determine the threshold. Therefore, the NTT DoCoMo QRD-M algorithm may need additional complexity for estimating the noise power and its complexity may be subject to change according to an error of the estimated noise power. In addition, when the threshold cannot be appropriately set, calculation for the last symbol estimation may increase.